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About

Pengxiang Zhu is a robotics and autonomous systems researcher whose work sits at the intersection of multi-robot coordination, state estimation, and mobile sensing. His research focuses primarily on cooperative localization, visual-inertial odometry, and active target tracking in distributed multi-robot networks — challenges fundamental to deploying autonomous systems in GPS-denied or unstructured environments. Zhu's most influential contribution, "Fully Distributed Joint Localization and Target Tracking With Mobile Robot Networks" (2020, 62 citations), established a scalable framework enabling robot teams to simultaneously localize themselves and track multiple targets without centralized coordination. This work, alongside his multi-robot visual-inertial odometry series — including Cooperative Visual-Inertial Odometry (25 citations) and its distributed variant (16 citations) — demonstrates his sustained effort to make cooperative perception both computationally practical and theoretically rigorous through tools like the Multi-State Constraint Kalman Filter and covariance intersection. More recently, Zhu has pushed toward active estimation, where robots intelligently plan their motions to improve localization and tracking accuracy, and has addressed low-feature environments through point-line fusion in PL-CVIO. Collectively, his publications reflect a coherent research vision: enabling robot teams to perceive and navigate complex environments reliably through principled distributed algorithms, making his work essential reading for researchers in autonomous robotics and multi-agent systems.